We are seeing a unification between data and microservice architectures: the demand for availability, scalability, and resilience is forcing fast data architectures to become like microservice architectures, while organizations building microservices find their data requirements are also evolving. At the center of it all is stream data processing, which is about more than just extracting information faster. It’s about embracing wholesale change in how organizations build data-centric applications.

Yet getting started with streaming and fast data systems provides a number of tough questions and challenges to enterprises, which we’ve encapsulated into five major categories:

Choosing among streaming frameworks. How to select the right stream processing frameworks (e.g. Akka Streams, Spark, Flink, Kafka Streams) for different use cases?

Integrating with application architecture. How to best integrate microservices with streaming data services?

Operational challenges. What do you need to know about deploying, managing and monitoring our application clusters in the long term?

Decreasing costs. How can you minimize costs by keeping our infrastructure footprint small, while not trading off performance?

Applying Machine Learning. How can you start using Machine Learning, Deep Learning and AI to your advantage?

In this webinar, Lightbend’s Senior Product Director, Craig Blitz reviews the implications of these decisions, and give you a preview of what Lightbend is doing to make these choices more straightforward with our upcoming Fast Data Platform - an integrated platform that helps you build, deploy and run Fast Data and streaming applications easily and reliably.

Additional Resources to Check Out

If you are a developer or architect looking to learn more, consider our Fast Data Platform Technical Overview or Fast Data Architectures for Streaming Applications, by Dean Wampler (VP of Fast Data Engineering at Lightbend). If you are a team lead or manager, review our Reactive Launch engagement and read some of our streaming and Fast Data success stories from enterprises like Credit Karma, Weight Watchers, Zalando, Swisscom, and Intel: